skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Enhanced compiler bug isolation via memoized search
Compiler bugs can be disastrous since they could affect all the software systems built on the buggy compilers. Meanwhile, diagnosing compiler bugs is extremely challenging since usually limited debugging information is available and a large number of compiler files can be suspicious. More specifically, when compiling a given bug-triggering test program, hundreds of compiler files are usually involved, and can all be treated as suspicious buggy files. To facilitate compiler debugging, in this paper we propose the first reinforcement compiler bug isolation approach via structural mutation, called RecBi. For a given bug-triggering test program, RecBi first augments traditional local mutation operators with structural ones to transform it into a set of passing test programs. Since not all the passing test programs can help isolate compiler bugs effectively, RecBi further leverages reinforcement learning to intelligently guide the process of passing test program generation. Then, RecBi ranks all the suspicious files by analyzing the compiler execution traces of the generated passing test programs and the given failing test program following the practice of compiler bug isolation. The experimental results on 120 real bugs from two most popular C open-source compilers, i.e., GCC and LLVM, show that RecBi is able to isolate about 23%/58%/78% bugs within Top-1/Top-5/Top-10 compiler files, and significantly outperforms the state-of-the-art compiler bug isolation approach by improving 92.86%/55.56%/25.68% isolation effectiveness in terms of Top-1/Top-5/Top-10 results.  more » « less
Award ID(s):
1942430 1763906
PAR ID:
10217483
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
IEEE/ACM International Conference on Automated Software Engineering
Page Range / eLocation ID:
78 to 89
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; (, ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering)
    Compiler bugs are extremely harmful, but are notoriously difficult to debug because compiler bugs usually produce few debugging information. Given a bug-triggering test program for a compiler, hundreds of compiler files are usually involved during compilation, and thus are suspect buggy files. Although there are lots of automated bug isolation techniques, they are not applicable to compilers due to the scalability or effectiveness problem. To solve this problem, in this paper, we transform the compiler bug isolation problem into a search problem, i.e., searching for a set of effective witness test programs that are able to eliminate innocent compiler files from suspects. Based on this intuition, we propose an automated compiler bug isolation technique, DiWi, which (1) proposes a heuristic-based search strategy to generate such a set of effective witness test programs via applying our designed witnessing mutation rules to the given failing test program, and (2) compares their coverage to isolate bugs following the practice of spectrum-based bug isolation. The experimental results on 90 real bugs from popular GCC and LLVM compilers show that DiWi effectively isolates 66.67%/78.89% bugs within Top-10/Top-20 compiler files, significantly outperforming state-of-the-art bug isolation techniques. 
    more » « less
  2. ; (, ACM Symposium on Cloud Computing 2021)
    Fault-isolation is extremely challenging in large scale data processing in cloud environments. Data provenance is a dominant existing approach to isolate data records responsible for a given output. However, data provenance concerns fault isolation only in the data-space, as opposed to fault isolation in the code-space---how can we precisely localize operations or APIs responsible for a given suspicious or incorrect result? We present OptDebug that identifies fault-inducing operations in a dataflow application using three insights. First, debugging is easier with a small-scale input than a large-scale input. So it uses data provenance to simplify the original input records to a smaller set leading to test failures and test successes. Second, keeping track of operation provenance is crucial for debugging. Thus, it leverages automated taint analysis to propagate the lineage of operations downstream with individual records. Lastly, each operation may contribute to test failures to a different degree. Thus OptDebug ranks each operation's spectra---the relative participation frequency in failing vs. passing tests. In our experiments, OptDebug achieves 100% recall and 86% precision in terms of detecting faulty operations and reduces the debugging time by 17x compared to a naïve approach. Overall, OptDebug shows great promise in improving developer productivity in today's complex data processing pipelines by obviating the need to re-execute the program repetitively with different inputs and manually examine program traces to isolate buggy code. 
    more » « less
  3. ; ; ; ; (, Proceedings of the ACM on Software Engineering)
    We present LeJit, a template-based framework for testing Java just-in-time (JIT) compilers. Like recent template-based frameworks, LeJit executes a template---a program with holes to be filled---to generate concrete programs given as inputs to Java JIT compilers. LeJit automatically generates template programs from existing Java code by converting expressions to holes, as well as generating necessary glue code (i.e., code that generates instances of non-primitive types) to make generated templates executable. We have successfully used LeJit to test a range of popular Java JIT compilers, revealing five bugs in HotSpot, nine bugs in OpenJ9, and one bug in GraalVM. All of these bugs have been confirmed by Oracle and IBM developers, and 11 of these bugs were previously unknown, including two CVEs (Common Vulnerabilities and Exposures). Our comparison with several existing approaches shows that LeJit is complementary to them and is a powerful technique for ensuring Java JIT compiler correctness. 
    more » « less
  4. ; (, Proceedings of the 17th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE ’21))
    null (Ed.)
    Many widely-deployed modern programming systems use just-in-time (JIT) compilers to improve performance. The size and complexity of JIT-based systems, combined with the dynamic nature of JIT-compiler optimizations, make it challenging to locate and fix JIT compiler bugs quickly. At the same time, JIT compiler bugs can result in exploitable security vulnerabilities, making rapid bug localization important. Existing work on automated bug localization focuses on static code, i.e., code that is not generated at runtime, and so cannot handle bugs in JIT compilers that generate incorrect code during optimization. This paper describes an approach to automated bug localization in JIT compilers, down to the level of distinct optimization phases, starting with a single initial Proof-of-Concept (PoC) input that demonstrates the bug. Experiments using a prototype implementation of our ideas on Google’s V8 JavaScript interpreter and TurboFan JIT compiler demonstrates that it can successfully identify buggy optimization phases. 
    more » « less
  5. ; ; (, Proceedings of the 11th ACM SIGPLAN International Workshop on the State Of the Art in Program Analysis)
    Gonnord, Laure; Titolo, Laura (Ed.)
    Just-in-Time (JIT) compilers are widely used to improve the performance of interpreter-based language implementations by creating optimized code at runtime. However, bugs in the JIT compiler’s code manipulation and optimization can result in the generation of incorrect code. Such bugs can be difficult to diagnose and fix, and can result in exploitable vulnerabilities. Unfortunately, existing approaches to automatic bug localization do not carry over well to such bugs. This paper discusses a different approach to analyzing JIT compiler optimization behaviors, based on using dynamic analysis to construct abstract models of the JIT compiler’s optimizer and back end. By comparing the models obtained for buggy and non-buggy executions of the JIT compiler, we can pinpoint the components of the JIT compiler’s internal representation that have been affected by the bug; this can then be mapped back to identify the buggy code. Our ex- periments with two real bugs for Google V8 JIT compiler, TurboFan, show the utility and practicality of our approach. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email